(dp0
S'scholar_data'
p1
(lp2
ccopy_reg
_reconstructor
p3
(cscholar
ScholarArticle
p4
c__builtin__
object
p5
Ntp6
Rp7
(dp8
S'citation_data'
p9
NsS'attrs'
p10
(dp11
S'num_versions'
p12
(lp13
I16
aS'Versions'
p14
aI4
asS'url_citation'
p15
(lp16
NaS'Citation link'
p17
aI9
asS'title'
p18
(lp19
VLearning appearance in virtual scenarios for pedestrian detection
p20
aS'Title'
p21
aI0
asS'url'
p22
(lp23
S'http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5540218'
p24
aS'URL'
p25
aI1
asS'url_versions'
p26
(lp27
S'http://scholar.google.com/scholar?cluster=17243485674852907889&hl=en&as_sdt=0,5'
p28
aS'Versions list'
p29
aI8
asS'excerpt'
p30
(lp31
VAbstract Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a  ...
p32
aS'Excerpt'
p33
aI10
asS'url_pdf'
p34
(lp35
NaS'PDF link'
p36
aI6
asS'num_citations'
p37
(lp38
I79
aS'Citations'
p39
aI3
asS'cluster_id'
p40
(lp41
S'17243485674852907889'
p42
aS'Cluster ID'
p43
aI5
asS'year'
p44
(lp45
V2010
p46
aS'Year'
p47
aI2
asS'url_citations'
p48
(lp49
S'http://scholar.google.com/scholar?cites=17243485674852907889&as_sdt=2005&sciodt=0,5&hl=en'
p50
aS'Citations list'
p51
aI7
assbag3
(g4
g5
Ntp52
Rp53
(dp54
g9
Nsg10
(dp55
g12
(lp56
I0
ag14
aI4
asg15
(lp57
Nag17
aI9
asg18
(lp58
VThe synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
p59
ag21
aI0
asg22
(lp60
S'http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Ros_The_SYNTHIA_Dataset_CVPR_2016_paper.html'
p61
ag25
aI1
asg26
(lp62
Nag29
aI8
asg30
(lp63
VAbstract Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks.  ...
p64
ag33
aI10
asg34
(lp65
Nag36
aI6
asg37
(lp66
I4
ag39
aI3
asg40
(lp67
S'9178628328030932213'
p68
ag43
aI5
asg44
(lp69
V2016
p70
ag47
aI2
asg48
(lp71
S'http://scholar.google.com/scholar?cites=9178628328030932213&as_sdt=2005&sciodt=0,5&hl=en'
p72
ag51
aI7
assbag3
(g4
g5
Ntp73
Rp74
(dp75
g9
Nsg10
(dp76
g12
(lp77
I3
ag14
aI4
asg15
(lp78
Nag17
aI9
asg18
(lp79
VVirtual Worlds as Proxy for Multi-Object Tracking Analysis
p80
ag21
aI0
asg22
(lp81
S'http://arxiv.org/abs/1605.06457'
p82
ag25
aI1
asg26
(lp83
S'http://scholar.google.com/scholar?cluster=11727455440906017188&hl=en&as_sdt=0,5'
p84
ag29
aI8
asg30
(lp85
VAbstract: Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual  ...
p86
ag33
aI10
asg34
(lp87
Nag36
aI6
asg37
(lp88
I5
ag39
aI3
asg40
(lp89
S'11727455440906017188'
p90
ag43
aI5
asg44
(lp91
V2016
p92
ag47
aI2
asg48
(lp93
S'http://scholar.google.com/scholar?cites=11727455440906017188&as_sdt=2005&sciodt=0,5&hl=en'
p94
ag51
aI7
assbag3
(g4
g5
Ntp95
Rp96
(dp97
g9
Nsg10
(dp98
g12
(lp99
I0
ag14
aI4
asg15
(lp100
Nag17
aI9
asg18
(lp101
VPlaying for data: Ground truth from computer games
p102
ag21
aI0
asg22
(lp103
S'http://link.springer.com/chapter/10.1007/978-3-319-46475-6_7'
p104
ag25
aI1
asg26
(lp105
Nag29
aI8
asg30
(lp106
VAbstract Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we  ...
p107
ag33
aI10
asg34
(lp108
Nag36
aI6
asg37
(lp109
I1
ag39
aI3
asg40
(lp110
S'12822958035144353200'
p111
ag43
aI5
asg44
(lp112
V2016
p113
ag47
aI2
asg48
(lp114
S'http://scholar.google.com/scholar?cites=12822958035144353200&as_sdt=2005&sciodt=0,5&hl=en'
p115
ag51
aI7
assbag3
(g4
g5
Ntp116
Rp117
(dp118
g9
Nsg10
(dp119
g12
(lp120
I0
ag14
aI4
asg15
(lp121
Nag17
aI9
asg18
(lp122
VPlay and learn: using video Games to train computer vision models
p123
ag21
aI0
asg22
(lp124
S'http://arxiv.org/abs/1608.01745'
p125
ag25
aI1
asg26
(lp126
Nag29
aI8
asg30
(lp127
VAbstract: Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world images to  ...
p128
ag33
aI10
asg34
(lp129
Nag36
aI6
asg37
(lp130
I1
ag39
aI3
asg40
(lp131
S'16081073673799361643'
p132
ag43
aI5
asg44
(lp133
V2016
p134
ag47
aI2
asg48
(lp135
S'http://scholar.google.com/scholar?cites=16081073673799361643&as_sdt=2005&sciodt=0,5&hl=en'
p136
ag51
aI7
assbag3
(g4
g5
Ntp137
Rp138
(dp139
g9
Nsg10
(dp140
g12
(lp141
I3
ag14
aI4
asg15
(lp142
Nag17
aI9
asg18
(lp143
VViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
p144
ag21
aI0
asg22
(lp145
S'http://arxiv.org/abs/1605.02097'
p146
ag25
aI1
asg26
(lp147
S'http://scholar.google.com/scholar?cluster=4101579648300742816&hl=en&as_sdt=0,5'
p148
ag29
aI8
asg30
(lp149
VAbstract: The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real- ...
p150
ag33
aI10
asg34
(lp151
Nag36
aI6
asg37
(lp152
I4
ag39
aI3
asg40
(lp153
S'4101579648300742816'
p154
ag43
aI5
asg44
(lp155
V2016
p156
ag47
aI2
asg48
(lp157
S'http://scholar.google.com/scholar?cites=4101579648300742816&as_sdt=2005&sciodt=0,5&hl=en'
p158
ag51
aI7
assbag3
(g4
g5
Ntp159
Rp160
(dp161
g9
Nsg10
(dp162
g12
(lp163
I6
ag14
aI4
asg15
(lp164
Nag17
aI9
asg18
(lp165
VA large dataset of object scans
p166
ag21
aI0
asg22
(lp167
S'http://arxiv.org/abs/1602.02481'
p168
ag25
aI1
asg26
(lp169
S'http://scholar.google.com/scholar?cluster=5989950372336055491&hl=en&as_sdt=0,5'
p170
ag29
aI8
asg30
(lp171
VAbstract: We have created a dataset of more than ten thousand 3D scans of real objects. To create the dataset, we recruited 70 operators, equipped them with consumer-grade mobile 3D scanning setups, and paid them to scan objects in their environments. The operators  ...
p172
ag33
aI10
asg34
(lp173
Nag36
aI6
asg37
(lp174
I6
ag39
aI3
asg40
(lp175
S'5989950372336055491'
p176
ag43
aI5
asg44
(lp177
V2016
p178
ag47
aI2
asg48
(lp179
S'http://scholar.google.com/scholar?cites=5989950372336055491&as_sdt=2005&sciodt=0,5&hl=en'
p180
ag51
aI7
assbag3
(g4
g5
Ntp181
Rp182
(dp183
g9
Nsg10
(dp184
g12
(lp185
I13
ag14
aI4
asg15
(lp186
Nag17
aI9
asg18
(lp187
VA Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
p188
ag21
aI0
asg22
(lp189
S'http://arxiv.org/abs/1512.02134'
p190
ag25
aI1
asg26
(lp191
S'http://scholar.google.com/scholar?cluster=16431759299155441580&hl=en&as_sdt=0,5'
p192
ag29
aI8
asg30
(lp193
VAbstract: Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset.  ...
p194
ag33
aI10
asg34
(lp195
Nag36
aI6
asg37
(lp196
I9
ag39
aI3
asg40
(lp197
S'16431759299155441580'
p198
ag43
aI5
asg44
(lp199
V2015
p200
ag47
aI2
asg48
(lp201
S'http://scholar.google.com/scholar?cites=16431759299155441580&as_sdt=2005&sciodt=0,5&hl=en'
p202
ag51
aI7
assbag3
(g4
g5
Ntp203
Rp204
(dp205
g9
Nsg10
(dp206
g12
(lp207
I7
ag14
aI4
asg15
(lp208
Nag17
aI9
asg18
(lp209
VRender for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
p210
ag21
aI0
asg22
(lp211
S'http://www.cv-foundation.org/openaccess/content_iccv_2015/html/Su_Render_for_CNN_ICCV_2015_paper.html'
p212
ag25
aI1
asg26
(lp213
S'http://scholar.google.com/scholar?cluster=1209553997502402606&hl=en&as_sdt=0,5'
p214
ag29
aI8
asg30
(lp215
VAbstract Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired by the growing availability of 3D models, we  ...
p216
ag33
aI10
asg34
(lp217
Nag36
aI6
asg37
(lp218
I33
ag39
aI3
asg40
(lp219
S'1209553997502402606'
p220
ag43
aI5
asg44
(lp221
V2015
p222
ag47
aI2
asg48
(lp223
S'http://scholar.google.com/scholar?cites=1209553997502402606&as_sdt=2005&sciodt=0,5&hl=en'
p224
ag51
aI7
assbag3
(g4
g5
Ntp225
Rp226
(dp227
g9
Nsg10
(dp228
g12
(lp229
I8
ag14
aI4
asg15
(lp230
Nag17
aI9
asg18
(lp231
VShapenet: An information-rich 3d model repository
p232
ag21
aI0
asg22
(lp233
S'http://arxiv.org/abs/1512.03012'
p234
ag25
aI1
asg26
(lp235
S'http://scholar.google.com/scholar?cluster=1341601736562194564&hl=en&as_sdt=0,5'
p236
ag29
aI8
asg30
(lp237
VAbstract: We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection  ...
p238
ag33
aI10
asg34
(lp239
Nag36
aI6
asg37
(lp240
I27
ag39
aI3
asg40
(lp241
S'1341601736562194564'
p242
ag43
aI5
asg44
(lp243
V2015
p244
ag47
aI2
asg48
(lp245
S'http://scholar.google.com/scholar?cites=1341601736562194564&as_sdt=2005&sciodt=0,5&hl=en'
p246
ag51
aI7
assbag3
(g4
g5
Ntp247
Rp248
(dp249
g9
Nsg10
(dp250
g12
(lp251
I12
ag14
aI4
asg15
(lp252
Nag17
aI9
asg18
(lp253
VVirtual and real world adaptation for pedestrian detection
p254
ag21
aI0
asg22
(lp255
S'http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6587038'
p256
ag25
aI1
asg26
(lp257
S'http://scholar.google.com/scholar?cluster=2637402509859183337&hl=en&as_sdt=0,5'
p258
ag29
aI8
asg30
(lp259
VAbstract\u2014Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, ie, trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to  ...
p260
ag33
aI10
asg34
(lp261
Nag36
aI6
asg37
(lp262
I46
ag39
aI3
asg40
(lp263
S'2637402509859183337'
p264
ag43
aI5
asg44
(lp265
V2014
p266
ag47
aI2
asg48
(lp267
S'http://scholar.google.com/scholar?cites=2637402509859183337&as_sdt=2005&sciodt=0,5&hl=en'
p268
ag51
aI7
assbag3
(g4
g5
Ntp269
Rp270
(dp271
g9
Nsg10
(dp272
g12
(lp273
I21
ag14
aI4
asg15
(lp274
Nag17
aI9
asg18
(lp275
VSeeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
p276
ag21
aI0
asg22
(lp277
S'http://www.cv-foundation.org/openaccess/content_cvpr_2014/html/Aubry_Seeing_3D_Chairs_2014_CVPR_paper.html'
p278
ag25
aI1
asg26
(lp279
S'http://scholar.google.com/scholar?cluster=18030645502969108287&hl=en&as_sdt=0,5'
p280
ag29
aI8
asg30
(lp281
VAbstract This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online. Using the" chair" class as a running example, we propose an  ...
p282
ag33
aI10
asg34
(lp283
Nag36
aI6
asg37
(lp284
I110
ag39
aI3
asg40
(lp285
S'18030645502969108287'
p286
ag43
aI5
asg44
(lp287
V2014
p288
ag47
aI2
asg48
(lp289
S'http://scholar.google.com/scholar?cites=18030645502969108287&as_sdt=2005&sciodt=0,5&hl=en'
p290
ag51
aI7
assbag3
(g4
g5
Ntp291
Rp292
(dp293
g9
Nsg10
(dp294
g12
(lp295
I13
ag14
aI4
asg15
(lp296
Nag17
aI9
asg18
(lp297
VDetailed 3d representations for object recognition and modeling
p298
ag21
aI0
asg22
(lp299
S'http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6516504'
p300
ag25
aI1
asg26
(lp301
S'http://scholar.google.com/scholar?cluster=6595507135181144034&hl=en&as_sdt=0,5'
p302
ag29
aI8
asg30
(lp303
VAbstract\u2014Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact  ...
p304
ag33
aI10
asg34
(lp305
Nag36
aI6
asg37
(lp306
I67
ag39
aI3
asg40
(lp307
S'6595507135181144034'
p308
ag43
aI5
asg44
(lp309
V2013
p310
ag47
aI2
asg48
(lp311
S'http://scholar.google.com/scholar?cites=6595507135181144034&as_sdt=2005&sciodt=0,5&hl=en'
p312
ag51
aI7
assbag3
(g4
g5
Ntp313
Rp314
(dp315
g9
Nsg10
(dp316
g12
(lp317
I0
ag14
aI4
asg15
(lp318
Nag17
aI9
asg18
(lp319
VA naturalistic open source movie for optical flow evaluation
p320
ag21
aI0
asg22
(lp321
S'http://link.springer.com/chapter/10.1007/978-3-642-33783-3_44'
p322
ag25
aI1
asg26
(lp323
Nag29
aI8
asg30
(lp324
VAbstract Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a  ...
p325
ag33
aI10
asg34
(lp326
Nag36
aI6
asg37
(lp327
I227
ag39
aI3
asg40
(lp328
S'15124407213489971559'
p329
ag43
aI5
asg44
(lp330
V2012
p331
ag47
aI2
asg48
(lp332
S'http://scholar.google.com/scholar?cites=15124407213489971559&as_sdt=20000005&sciodt=0,21&hl=en'
p333
ag51
aI7
assbag3
(g4
g5
Ntp334
Rp335
(dp336
g9
Nsg10
(dp337
g12
(lp338
I4
ag14
aI4
asg15
(lp339
Nag17
aI9
asg18
(lp340
VOvvv: Using virtual worlds to design and evaluate surveillance systems
p341
ag21
aI0
asg22
(lp342
S'http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270516'
p343
ag25
aI1
asg26
(lp344
S'http://scholar.google.com/scholar?cluster=3459961090644684583&hl=en&as_sdt=0,5'
p345
ag29
aI8
asg30
(lp346
VAbstract ObjectVideo Virtual Video (OVVV) is a publicly available visual surveillance simulation test bed based on a commercial game engine. The tool simulates multiple synchronized video streams from a variety of camera configurations, including static, PTZ  ...
p347
ag33
aI10
asg34
(lp348
Nag36
aI6
asg37
(lp349
I58
ag39
aI3
asg40
(lp350
S'3459961090644684583'
p351
ag43
aI5
asg44
(lp352
V2007
p353
ag47
aI2
asg48
(lp354
S'http://scholar.google.com/scholar?cites=3459961090644684583&as_sdt=2005&sciodt=0,5&hl=en'
p355
ag51
aI7
assbag3
(g4
g5
Ntp356
Rp357
(dp358
g9
Nsg10
(dp359
g12
(lp360
I0
ag14
aI4
asg15
(lp361
Nag17
aI9
asg18
(lp362
VUnrealCV: Connecting Computer Vision to Unreal Engine
p363
ag21
aI0
asg22
(lp364
S'http://arxiv.org/abs/1609.01326'
p365
ag25
aI1
asg26
(lp366
Nag29
aI8
asg30
(lp367
VAbstract: Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which:(i) an agent can perceive, navigate, and take actions guided by AI algorithms,(ii) properties of the worlds can be  ...
p368
ag33
aI10
asg34
(lp369
Nag36
aI6
asg37
(lp370
I0
ag39
aI3
asg40
(lp371
Nag43
aI5
asg44
(lp372
V2016
p373
ag47
aI2
asg48
(lp374
Nag51
aI7
assbag3
(g4
g5
Ntp375
Rp376
(dp377
g9
Nsg10
(dp378
g12
(lp379
I2
ag14
aI4
asg15
(lp380
Nag17
aI9
asg18
(lp381
VLearning Physical Intuition of Block Towers by Example
p382
ag21
aI0
asg22
(lp383
S'http://arxiv.org/abs/1603.01312'
p384
ag25
aI1
asg26
(lp385
S'http://scholar.google.com/scholar?cluster=12846348306706460250&hl=en&as_sdt=0,5'
p386
ag29
aI8
asg30
(lp387
VAbstract: Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics. Using a 3D game  ...
p388
ag33
aI10
asg34
(lp389
Nag36
aI6
asg37
(lp390
I12
ag39
aI3
asg40
(lp391
S'12846348306706460250'
p392
ag43
aI5
asg44
(lp393
V2016
p394
ag47
aI2
asg48
(lp395
S'http://scholar.google.com/scholar?cites=12846348306706460250&as_sdt=2005&sciodt=0,5&hl=en'
p396
ag51
aI7
assbag3
(g4
g5
Ntp397
Rp398
(dp399
g9
Nsg10
(dp400
g12
(lp401
I0
ag14
aI4
asg15
(lp402
Nag17
aI9
asg18
(lp403
VTarget-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
p404
ag21
aI0
asg22
(lp405
S'http://arxiv.org/abs/1609.05143'
p406
ag25
aI1
asg26
(lp407
Nag29
aI8
asg30
(lp408
VAbstract: Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency ie, the model requires several (and often costly) episodes of trial and error to converge, which makes it  ...
p409
ag33
aI10
asg34
(lp410
Nag36
aI6
asg37
(lp411
I0
ag39
aI3
asg40
(lp412
Nag43
aI5
asg44
(lp413
V2016
p414
ag47
aI2
asg48
(lp415
Nag51
aI7
assbasS'paper_list'
p416
(lp417
(dp418
S'tag'
p419
S'transfer'
p420
sS'title'
p421
S'Learning appearance in virtual scenarios for pedestrian detection.'
p422
sa(dp423
S'project'
p424
S'http://synthia-dataset.net/'
p425
sS'tag'
p426
S'dataset'
p427
sS'title'
p428
S'The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes.'
p429
sa(dp430
S'project'
p431
S'http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds'
p432
sS'tag'
p433
S'dataset'
p434
sS'title'
p435
S'Virtual Worlds as Proxy for Multi-Object Tracking Analysis.'
p436
sa(dp437
S'project'
p438
NsS'tag'
p439
S'dataset'
p440
sS'title'
p441
S'Playing for data: Ground truth from computer games.'
p442
sa(dp443
S'project'
p444
NsS'tag'
p445
S'dataset'
p446
sS'title'
p447
S'Play and Learn: Using Video Games to Train Computer Vision Models.'
p448
sa(dp449
S'project'
p450
S'http://vizdoom.cs.put.edu.pl/'
p451
sS'code'
p452
S'https://github.com/Marqt/ViZDoom'
p453
sS'tag'
p454
S'tool'
p455
sS'title'
p456
S'ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning.'
p457
sa(dp458
S'project'
p459
S'http://redwood-data.org/3dscan/'
p460
sS'tag'
p461
S'model'
p462
sS'title'
p463
S'A large dataset of object scans.'
p464
sa(dp465
S'project'
p466
NsS'tag'
p467
S'dataset'
p468
sS'title'
p469
S'A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation.'
p470
sa(dp471
S'code'
p472
S'https://github.com/ShapeNet/RenderForCNN'
p473
sS'tag'
p474
S'tool, transfer'
p475
sS'title'
p476
S'Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views.'
p477
sa(dp478
S'project'
p479
S'http://shapenet.cs.stanford.edu/'
p480
sS'tag'
p481
S'model'
p482
sS'title'
p483
S'Shapenet: An information-rich 3d model repository.'
p484
sa(dp485
S'tag'
p486
S'transfer'
p487
sS'title'
p488
S'Virtual and real world adaptation for pedestrian detection.'
p489
sa(dp490
S'project'
p491
S'http://www.di.ens.fr/willow/research/seeing3Dchairs/'
p492
sS'code'
p493
S'https://github.com/dimatura/seeing3d'
p494
sS'title'
p495
S'Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models.'
p496
sa(dp497
S'title'
p498
S'Detailed 3d representations for object recognition and modeling.'
p499
sa(dp500
S'project'
p501
S'http://sintel.is.tue.mpg.de/'
p502
sS'tag'
p503
S'optical flow, sintel, dataset'
p504
sS'cluster_id'
p505
L15124407213489971559L
sS'title'
p506
S'A naturalistic open source movie for optical flow evaluation.'
p507
sa(dp508
S'title'
p509
S'Ovvv: Using virtual worlds to design and evaluate surveillance systems.'
p510
sa(dp511
S'comment'
p512
S'My personal project'
p513
sS'code'
p514
S'http://unrealcv.github.io'
p515
sS'tag'
p516
S'tool, diagnosis'
p517
sS'title'
p518
S'UnrealCV: Connecting Computer Vision to Unreal Engine'
p519
sa(dp520
S'code'
p521
S'https://github.com/facebook/UETorch'
p522
sS'tag'
p523
S'tool'
p524
sS'title'
p525
S'Learning Physical Intuition of Block Towers by Example'
p526
sa(dp527
S'title'
p528
S'Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning'
p529
sas.